1. Field of the Invention
This invention relates to an autonomous control system for small unmanned helicopters, and autonomous control algorithms that control the rudders for said small unmanned helicopter based on the aforementioned mathematical models.
2. Description of the Prior Art
Helicopters are flying bodies that have operating ranges such as longitudinal motions, lateral motions, vertical motions, and hovering, which are not exhibited by an aircraft; as such, they have the advantage of being able to flexibly respond to various situations. Based on this advantage, there are hopes for constructing unmanned, small helicopters for use in places that are difficult or dangerous for manned operations, for example, in high-altitude work, such as the inspection of power transmission lines, or in emergency rescue operations or the detection of land mines. Previously, an autonomous control system using an unmanned helicopter for agricultural chemical spray applications was described in Patent Reference 1 listed below:
[Patent Reference 1]
Unexamined Japanese Patent Application Publication 2000-118498
First, let us provide a logical explanation of the mechanism of autonomous control for helicopters. Helicopters are the objects of control; they are flying bodies that are capable of changing their orientation by means of servo motor actions and are capable of three-dimensional motions. The purpose of flying by autonomous control is to move the helicopter according to positional and speed target values. The required maneuvering follows the computational results generated by calculation computer. In order to delegate the piloting of a helicopter to a computer, the calculation computer must have sensing and actuation functions. Devices that have the function of sensing the various flight conditions of a helicopter are called sensors. Actuators that move the helicopter's rudders by receiving autonomous control signals that are generated by determining control reference values based on computational results from the computer and by converting these results into signals are referred to as servo motors. The helicopter can be autonomously controlled toward a given target value by means of a feedback control loop that links “(sensor)-(calculation computer)-(servo motor)-(helicopter), Reference FIG. 1”.
Following is a description of autonomous control for a small unmanned helicopter described in Patent Reference 1, with reference FIG. 2 to drawings. This system can be divided into a mobile station, which includes the helicopter and a ground station. Mounted on the ground station are a helicopter body 101; a sensor 102 that detects the current position and attitude angle of the helicopter body 101; servo motors 103 that move the rudders for the helicopter body 101; a backup receiver 104 that receives manual maneuver signals from a backup transmitter 110; and a wireless modem that communicates with the ground station. Employed in the sensor 1021 are the GPS (not shown in the figure) that detects the current position of the helicopter body 101 and tri-axis orientation sensors (not shown in the figure) that detect the tri-axial attitude angles of the helicopter body 101. Installed on the ground station are a computer CPU 108 for the input of reference values on which speed reference values are entered; a computer CPU 109 for internal computation purposes; and a backup transmitter 110 that permits the operator to perform manual operations in the event of the occurrence of a dangerous situation. The CPU 109 calculates position and attitude angle reference values from target speed values, compares the results with the current position, speed, and attitude angle obtained from the sensors 102, and based on these results, calculates control instruction values that bring the helicopter to the reference values. By forming a feedback control loop by linking “(sensors 102)-(CPU 109)-(servo motors 103)-(helicopter body 101)” (intervening components omitted), it is possible to effect the autonomous control of the helicopter toward its reference values.
Following is a description of the operation of the system. The operator sets four speed reference values (Vx*, Vy*, Vz*, ω*) consisting of longitudinal, lateral, vertical, and rotational speeds, on the CPU 108. The CPU 109 integrates these speed reference values with respect to time, obtaining a longitudinal target position X*, a lateral target position Y*, a vertical target position Z*, and a rotational target position (yawing angle) ψ*.Similarly, the CPU109 differentiates the four speed reference values (Vx*, Vy*, Vz*, ω*) and multiplies the results by coefficients to calculate a target pitching angle θ* and a target rolling angle φ*. The differences between the target values that are set in this manner and the detected values (θ, φ, ψ, ω) for the body attitude, speed (X, Y, Z, Vx, Vy, Vz) that are detected by the sensors 102 consisting of the GPS and tri-axis attitude sensors that are installed on the helicopter body 101 are calculated as follows:ΔX=X*−XΔY=Y*−YΔZ=Z*−ZΔVx=Vx*−VxΔVy=Vy*−VyΔVz=Vz*−VzΔθ=θ*−θΔφ=φ*−φΔψ=ψ*−ψΔω=ω*−ω
Based upon these differences (errors), the CPU 109 calculates control reference values for the servo motors 103 that move the rudders for the helicopter body 101. Four types of control reference values are computed: elevator servo (longitudinal) instruction, aileron servo (lateral) instruction, corrective servo (vertical) instruction, and rudder servo (rotational) instruction. After computing these four types of control reference values, the CPU 109 supplies them to the aforementioned servo motors 103, and performs feedback control on these operations until the differences become zero (0).
Helicopters that are used in the aforementioned conventional autonomous control system were originally intended for the spraying of agricultural chemicals, with a maximum weight of approximately 30 kg. Although the aforementioned sensors and computational unit for the aforementioned conventional autonomous control system are large and heavy, the helicopter can adequately fly even when carrying these items. However, the unloaded helicopter used in the aforementioned conventional autonomous control system weights approximately 60 kg empty, and approximately 90 kg when fully loaded. Therefore, such a helicopter cannot easily be carried. In addition, in order to use the system, the helicopter must have a flight range sufficiently larger than the actual helicopter, which limits the range over which the helicopter can be deployed. In some cases, manned helicopter operations involve a narrow space in which the aforementioned conventional helicopter cannot negotiate. On the other hand, the small unmanned helicopter addressed in the present invention refers to a helicopter comparable in size and weight to, and compatible with, a commercially available hobby-type small-scale radio-controlled helicopter. Although the above problem can be solved by effecting autonomous control in such a helicopter, the smaller the weight of a helicopter, the more difficult it is to control. In other words, the autonomous control system is subject to stringent constraints in terms of size and weight, and small helicopters tend to be unstable in terms of dynamic properties. Therefore, with the aforementioned conventional autonomous control system, it is impossible to mount the autonomous control system on the aforementioned small unmanned helicopter as is. Further, applying the autonomous control algorithms for the aforementioned conventional autonomous control system to the aforementioned small unmanned helicopter as is does not guarantee adequate control performance. Further, the calculation of a control instruction value is a time-consuming process due to a large number of computational steps involved in the determination of servo motor control instruction values; consequently, when one attempts to achieve size reductions in the autonomous control system, one must contend with the conflicting requirements of accommodating a large number of computational steps and the stringent constraints imposed on the capabilities of the computational equipment and the size of the control program. Further, beyond the computational equipment, the sensors are also subject to stringent constraints on size and weight, which clearly adds to the difficulty of construction of small and lightweight autonomous control systems. An autonomous control system that can be mounted and flown on the type of small unmanned helicopter for which the present invention is intended has not been successfully developed. Manual operation being independent from autonomous control, can be thought of as being completely unrelated to autonomous control. However, the process of designing an autonomous control algorithm may require the measurement of manual operation signals. In creating mathematical models in the present invention, we used a technique called system characterization, wherein a mathematical model is obtained by associating input signals into the aforementioned servo motors for the aforementioned small unmanned helicopter with output signals, which represent the flying conditions of the aforementioned small unmanned helicopter as measured by sensors installed on the aforementioned autonomous control system, and by analyzing the signals. The system characterization requires the collection of input/output data under the condition in which the aforementioned small unmanned helicopter is flying, and this process is referred to as a characterization experiment. Conducting a characterization experiment requires that the aforementioned servo motors be driven using characterization input signals that are appropriate for system characterization. In such a case, however, characterization input by itself can cause a substantial tilt in the attitude of the aforementioned small unmanned helicopter and sudden acceleration, with a potential risk of accidents. To prevent such a problem, it is necessary to stabilize the motion of the aforementioned small unmanned helicopter by controlling the correction rudder by means of manual operation. However, because the correction rudder is considered to be part of characterization input, for system characterization, the aforementioned manual operation signals must also be obtained as measurement data.
As stated above, the present invention also takes into consideration the use of the aforementioned autonomous control unit as an auxiliary unit for manual operation, i.e., as an operator assist unit. In terms of the objective and the technique of operation assistance, manual operation signals, for example, could be associated with the target value input signals for the aforementioned autonomous control algorithm, and the drive signals that are actually output to the aforementioned servo motors could all be treated as autonomous control signals that are computational results of the aforementioned autonomous control algorithm. In other words, the method may be described as follows: even though the human operator may have the illusion of operating the helicopter himself, in actuality, he merely provides motion commands, which are target values, to the aforementioned autonomous control small unmanned helicopter; upon receipt of the target values, the aforementioned autonomous control algorithm is calculated, and autonomous control is effected. The method can provide the aforementioned autonomous control algorithm with target values without using the aforementioned ground station computer, with the advantage that people not versed in computer operation can safely fly the aforementioned small unmanned helicopter, enjoying the benefits of the aforementioned autonomous control algorithm. This approach, however, requires a new technique for associating the aforementioned manual operation signals with target values.
The development of autonomous control algorithms for the autonomous control of the aforementioned small unmanned helicopter requires mathematical models that describe the dynamic characteristics of the helicopter. The use of mathematical models permits the application of various control theories, which have made strides in recent years and whose effectiveness has amply been recognized, to the development of autonomous control algorithms, which should improve the flight performance in situations in which the aforementioned small unmanned helicopter is controlled autonomously.
However, the dynamic characteristics of a helicopter are subject to a complex interplay of dynamical action and fluid dynamic action, which makes analysis an extremely difficult task. Although a detailed analysis of the dynamic characteristics of manned helicopters has been pursued aggressively, little detailed analysis has been performed with regard to helicopters of the size addressed in the present invention. In addition, there have been no reports on the dynamic characteristics of the aforementioned servo motors.
Even if a mathematical model that describes the dynamic characteristics of the aforementioned small unmanned helicopter in detail exists, if the mathematical model is a highly complex one, the development of an autonomous control algorithm will also be difficult. Autonomous control algorithms that are developed and based on complex mathematical models are generally complex, and may not necessarily be appropriate for execution by a computer that is subject to stringent restrictions on its computational capabilities due to weight limitations.
There is a system identification method that avoids theoretical analyses and draws inferences on the dynamic characteristics of a given physical system based on its input/output relationships. System identification requires the input of signals containing frequency components encompassing a broad bandwidth into the physical system. However, entering such signals into the aforementioned small unmanned helicopter involves a risk. In addition, such a system will also require devices for the measurement of the aforementioned input/output signals and an instrumentation system. There have been no cases where system identification is run on the type of small unmanned helicopter addressed in the present invention and where the soundness of the identification model thus obtained is validated.